FERNANDO MITSUO SUMIYA
Projetos de Pesquisa
Unidades Organizacionais
LIM/23 - Laboratório de Psicopatologia e Terapêutica Psiquiátrica, Hospital das Clínicas, Faculdade de Medicina
4 resultados
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Agora exibindo 1 - 4 de 4
- Autism Spectrum Disorder diagnosis based on trajectories of eye tracking data(2021) V, Thiago Cardoso; MICHELASSI, Gabriel C.; FRANCO, Felipe O.; SUMIYA, Fernando M.; PORTOLESE, Joana; BRENTANI, Helena; MACHADO-LIMA, Ariane; NUNES, Fatima L. S.The use of Eye Tracking (ET) has been investigated as an auxiliary mechanism to diagnose Autism Spectrum Disorder (ASD). One of the paradigms investigated using ET is Joint Attention (JA), which refers to moments when two individuals are focused on the same object/event so that both are aware that the focus of attention is shared. The computational tools that assist in the diagnosis of ASD have used Image Processing and Machine Learning techniques to process images, videos and ET signals. However, the JA paradigm is still little explored and presents challenges, as it requires analyzing the gaze trajectory and needs innovative approaches. The purpose of this article is to propose a model capable of extracting features from a video used as a stimulus to capture ET signals in order to verify JA and classify individuals as belonging to the ASD or Typical Development (TD) group. The main differential in relation to the approaches in the literature is the definition and implementation of the concept of floating Regions of Interest, which allows monitoring the gaze in relation to an object, considering its semantics, even if the object presents different characteristics throughout the video. A model based on ensembles of Random Forest classifiers was implemented to classify individuals as ASD or TD using the trajectory features extracted from the ET signals. The method reached 0.75 accuracy and 0.82 F1-score, indicating that the proposed approach, based on trajectory and JA, has the potential to be applied to assist in the diagnosis of ASD.
- CRISIS AFAR: an international collaborative study of the impact of the COVID-19 pandemic on mental health and service access in youth with autism and neurodevelopmental conditions(2023) VIBERT, Bethany; SEGURA, Patricia; GALLAGHER, Louise; GEORGIADES, Stelios; PERVANIDOU, Panagiota; THURM, Audrey; ALEXANDER, Lindsay; ANAGNOSTOU, Evdokia; AOKI, Yuta; BIRKEN, Catherine S.; BISHOP, Somer L.; BOI, Jessica; BRAVACCIO, Carmela; BRENTANI, Helena; CANEVINI, Paola; CARTA, Alessandra; CHARACH, Alice; COSTANTINO, Antonella; COST, Katherine T.; CRAVO, Elaine A.; CROSBIE, Jennifer; DAVICO, Chiara; DONNO, Federica; FUJINO, Junya; GABELLONE, Alessandra; GEYER, Cristiane T.; HIROTA, Tomoya; KANNE, Stephen; KAWASHIMA, Makiko; KELLEY, Elizabeth; KIM, Hosanna; KIM, Young Shin; KIM, So Hyun; KORCZAK, Daphne J.; LAI, Meng-Chuan; MARGARI, Lucia; MARZULLI, Lucia; MASI, Gabriele; MAZZONE, Luigi; MCGRATH, Jane; MONGA, Suneeta; MOROSINI, Paola; NAKAJIMA, Shinichiro; NARZISI, Antonio; NICOLSON, Rob; NIKOLAIDIS, Aki; NODA, Yoshihiro; NOWELL, Kerri; POLIZZI, Miriam; PORTOLESE, Joana; RICCIO, Maria Pia; SAITO, Manabu; SCHWARTZ, Ida; SIMHAL, Anish K.; SIRACUSANO, Martina; SOTGIU, Stefano; STROUD, Jacob; SUMIYA, Fernando; TACHIBANA, Yoshiyuki; TAKAHASHI, Nicole; TAKAHASHI, Riina; TAMON, Hiroki; TANCREDI, Raffaella; VITIELLO, Benedetto; ZUDDAS, Alessandro; LEVENTHAL, Bennett; MERIKANGAS, Kathleen; MILHAM, Michael P.; MARTINO, Adriana DiBackgroundHeterogeneous mental health outcomes during the COVID-19 pandemic are documented in the general population. Such heterogeneity has not been systematically assessed in youth with autism spectrum disorder (ASD) and related neurodevelopmental disorders (NDD). To identify distinct patterns of the pandemic impact and their predictors in ASD/NDD youth, we focused on pandemic-related changes in symptoms and access to services.MethodsUsing a naturalistic observational design, we assessed parent responses on the Coronavirus Health and Impact Survey Initiative (CRISIS) Adapted For Autism and Related neurodevelopmental conditions (AFAR). Cross-sectional AFAR data were aggregated across 14 European and North American sites yielding a clinically well-characterized sample of N = 1275 individuals with ASD/NDD (age = 11.0 +/- 3.6 years; n females = 277). To identify subgroups with differential outcomes, we applied hierarchical clustering across eleven variables measuring changes in symptoms and access to services. Then, random forest classification assessed the importance of socio-demographics, pre-pandemic service rates, clinical severity of ASD-associated symptoms, and COVID-19 pandemic experiences/environments in predicting the outcome subgroups.ResultsClustering revealed four subgroups. One subgroup-broad symptom worsening only (20%)-included youth with worsening across a range of symptoms but with service disruptions similar to the average of the aggregate sample. The other three subgroups were, relatively, clinically stable but differed in service access: primarily modified services (23%), primarily lost services (6%), and average services/symptom changes (53%). Distinct combinations of a set of pre-pandemic services, pandemic environment (e.g., COVID-19 new cases, restrictions), experiences (e.g., COVID-19 Worries), and age predicted each outcome subgroup.LimitationsNotable limitations of the study are its cross-sectional nature and focus on the first six months of the pandemic.ConclusionsConcomitantly assessing variation in changes of symptoms and service access during the first phase of the pandemic revealed differential outcome profiles in ASD/NDD youth. Subgroups were characterized by distinct prediction patterns across a set of pre- and pandemic-related experiences/contexts. Results may inform recovery efforts and preparedness in future crises; they also underscore the critical value of international data-sharing and collaborations to address the needs of those most vulnerable in times of crisis.
- Computer-aided autism diagnosis using visual attention models and eye-tracking: replication and improvement proposal(2023) FRANCO, Felipe O.; OLIVEIRA, Jessica S.; PORTOLESE, Joana; SUMIYA, Fernando M.; SILVA, Andreia F.; MACHADO-LIMA, Ariane; NUNES, Fatima L. S.; BRENTANI, HelenaBackgroundAutism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset; 3) to evaluate the performance of classifiers obtained by a new classification algorithm (Random Forest) using the previous and the current datasets.MethodsThe previously approach was replicated with a new stimulus and new sample, 44 from the Typical Development group and 33 from the ASD group. After the replication, Random Forest classifier was tested to substitute Neural Networks algorithm.ResultsThe test with the trained classifier reached an AUC of 0.56, suggesting that the trained classifier requires retraining of the VAMs when changing the stimulus. The replication results reached an AUC of 0.71, indicating the potential of generalization of the approach for aiding ASD diagnosis, as long as the stimulus is similar to the originally proposed. The results achieved with Random Forest were superior to those achieved with the original approach, with an average AUC of 0.95 for the previous dataset and 0.74 for the new dataset.ConclusionIn summary, the results of the replication experiment were satisfactory, which suggests the robustness of the approach and the VAM-based approaches feasibility to aid in ASD diagnosis. The proposed method change improved the classification performance. Some limitations are discussed and additional studies are encouraged to test other conditions and scenarios.
- A systematic review of observational, naturalistic, and neurophysiological outcome measures of nonpharmacological interventions for autism(2022) GODOY, Priscilla Brandi Gomes; SUMIYA, Fernando Mitsuo; SEDA, Leonardo; SHEPHARD, ElizabethObjective: Naturalistic and neurophysiological assessments are relevant as outcome measures in autism intervention trials because they provide, respectively, ecologically valid information about functioning and underlying neurocognitive mechanisms. We conducted a systematic review to highlight which specific neurophysiological techniques, experimental tasks, and naturalistic protocols have been used to assess neural and behavioral functioning in autism intervention studies.Methods: Studies were collected from four electronic databases between October 2019 and February 2020: MEDLINE (via PubMed), PsycINFO, LILACS, and Web of Science, and were included if they used structured observational, naturalistic, or neurophysiological measures to assess the efficacy of a nonpharmacological intervention for ASD.Results: Fourteen different measures were used by 64 studies, with the Autism Diagnostic Observation Schedule the most frequently used instrument. Thirty-seven different coding systems of naturalistic measures were used across 51 studies, most of which used different protocols. Twenty-four neurophysiological measures were used in 16 studies, with different experimental paradigms and neurophysiological components used across studies.Conclusions: Cross-study variability in assessing the outcomes of autism interventions may obscure comparisons and conclusions about how different behavioral interventions affect autistic social communication and underlying neurophysiological mechanisms.